in High Performance Computing Victor Eijkhout CppCon 2024 Introduction This poster reports on ‘D2D’, a benchmark that explores elegance of expression and perfor- mance in the context of a High Performance implemented using a number of different approaches to parallelism. Implementations are discussed with performance results. Relevance C++ is making inroads into HPC / Scientific Computing, a field traditionally multi-dimensional arrays through ‘mdspan’, it is interesting to explore what C++ can offer for lower level performance critical operations. Scientific computing is an interesting test cases since many algorithms are

Introduction Firsts steps Context Theoretical foundations Outline of an implementation Conclusion High-Performance Numerical Integration in the Age of C++26 Vincent Reverdy Laboratoire d’Annecy de Physique past, other languages do far better in terms of everything: functionality, ease of use, and even performance This talk The goal is NOT to revolutionize everything or show a library that beats everything Verner’s methods Explicit adaptive Runge-Kutta schemes Slightly enhanced methods with interpolants High-accurary interpolation inside integration steps Interpolation with extra coefficients βi yn+u =

Nim - the first high performance Nim - the first high performance language with full support for hot code- language with full support for hot code- reloading at runtime reloading at runtime by Viktor Iterators type CustomRange = object low: int high: int iterator items(range: CustomRange): int = var i = range.low while i <= range.high: yield i inc i iterator pairs(range: CustomRange): range: # uses CustomRange.items yield (i, char(i + ord('a'))) for i, c in CustomRange(low: 1, high: 3): echo c # prints: b, c, d 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 7 Variants Variants

is moved into general-purpose registers for computations • Depending on the platform, may see a performance gain at this stageQuat Functions template inline

Binding Constraints Architecture Substitution Construction Conclusion Symbolic Calculus for High-Performance Computing from Scratch using C++23 Vincent Reverdy Laboratoire d’Annecy de Physique des Particules all know about optimization, performance, parallelism, . . . What this talk is not about Complicated maths (you are smart people, you can do it yourself) High-performance computing (you all know about concepts Symbolic calculus (derivatives, integrals) Full blown custom rule-based rewriting High-performance Since formulas have the entire information on the mathematical AST, it’s possible to generate

Performance Lets dive into Performance issues. • Everything in JavaScript defaults to being on the same thread. Too much work on main thread • Android nested layouts • Functions and objects defined

PERFORMANCE MATTERS (joint work with Charlie Curtsinger, Grinnell College) emeryberger.com, @emeryberger Emery Berger College of Information and Computer Sciences UMASS AMHERSTA short time ago : un.bmp Ogle is too slow! OGLE’84 is too slow!Transistors (millions) Clock Speed (MHz) Performance used to be easy 0.001 0.01 0.1 1 10 100 1,000 10,000 1970 1975 1980 1985 1990 1995 gle loading… No mojitos for me… Back to the present…Transistors (millions) Clock Speed (MHz) Performance not easy anymore 0.001 0.01 0.1 1 10 100 1,000 10,000 1970 1975 1980 1985 1990 1995

Performance of Apache Ozone on NVMe Wei-Chiu Chuang (jojochuang) Ritesh Shukla (kerneltime) Agenda • Overview of how Ozone and how it scales • Why NVME is important for Ozone for scaling • Benefits Benefits of using NVME • Impala performance results from NVME clusters • Write path improvements results from NVME clusters • Summary • Questions Ozone Architecture Why does Ozone Scale? Separation of restart) Slow startup based on size High Availability Active - Active Active - Standby Protocol Support Hadoop / S3 API Hadoop API Small objects are welcome Max performance reached at object size around

Being Friendly to Your Hardware Performance Engineering A gentle introduction to hardware for software engineers 2Where does C++ run? 3On an abstract C++ machine 4On an abstract C++ machine? multilayer componentsMemory • What about HBM? Not 1024 bits, 8 x 128 instead. Bandwidth will only be high when there is sufficient stream of commands inflight • What about remote memory? CXL and vendor In most practical cases at boot time only Same capacity, different composition => different performance profile From JESD 79-4 DDR4 specificationMemory • Memory system is in the uncore • Cores act

WHITEPAPER How GitOps Boosts Business Performance: The Facts How GitOps Boosts Business Performance: The Facts 2 INTRODUCTION As cloud-native applications have become more prevalent, the concept competitive advantage with an increase in innovation. This positive effect is not limited to the performance of engineering teams. Technology, in particular cloud native technology like Kubernetes and its together six years of data drawn from over 31,000 technology professionals worldwide. It charts the performance of engineering teams across the world against four key measures: lead time for new features, failure